spike
Datasets
All datasets matching “spike”SpikeStereoNettaskcompendium-spike
TaskCompendium spike examples
Schema 0.9 contains 63 semantic TaskSpec records and 164 Harbor lowerings. A TaskSpec defines the problem, semantic requirements, provenance, and private correctness contract. A lowering chooses model-visible instructions, result rendering, target binding, and public tools. It does not choose a model or harness.
from datasets import load_dataset
specifications = load_dataset("open-athena/taskcompendium-spike", "specifications", split="examples")… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/taskcompendium-spike.SemanticVLA-TraceX-240K-DROID
SemanticVLA TraceX 240K · DROID
🎉 Accepted to CVPR 2026.
✍️ Fei Ni¹, Zhuo Chen², Yifu Yuan³, Zibin Dong³, Xianze Yao³, Shan Luo², Jianye Hao³, Jiankang Deng¹†, Stefanos Zafeiriou¹†
🏫 ¹Imperial College London ²King's College London ³Tianjin University
✉️ Primary contact: f.ni@imperial.ac.uk
The DROID component of TraceX-240K — the trace-annotated trajectory corpus introduced in SemanticVLA. This package is a LeRobot v3.0 repack of DROID · Franka · Open-X-Embodiment DROID… See the full description on the dataset page: https://huggingface.co/datasets/spikefly/SemanticVLA-TraceX-240K-DROID.mathlib-exportspikeprophecy-steinmetz
SpikeProphecy / Steinmetz 2019 (processed)
Processed 50 ms-binned spike-count tensors derived from the
publicly released Steinmetz 2019 dataset, used as the primary
substrate of the SpikeProphecy benchmark
(NeurIPS 2026 Datasets & Benchmarks track).
This dataset is a deterministic preprocessing of the
publicly released Steinmetz 2019 recordings. The source data
(raw spike times, behavioral covariates, NWB files) remain at
their canonical Figshare home and are not redistributed… See the full description on the dataset page: https://huggingface.co/datasets/mysteriousauthor/spikeprophecy-steinmetz.Spikenaut-SNN-Telemetry
🧠 Spikenaut SNN Telemetry Dataset
"The threshold at which stimulus becomes perceptible"
Telemetry for the Spikenaut Supervisor control stack: v3 restructures this
corpus from time-series forecasting into an action-proposal trajectory
dataset — states, proposed actions, safety-filter verdicts, and outcomes —
while every v2 config remains published, byte-identical and loadable.
The control hierarchy this dataset serves:
learned policy → action proposal → deterministic safety… See the full description on the dataset page: https://huggingface.co/datasets/rmems/Spikenaut-SNN-Telemetry.